E-VOC: Expressive VOice Control Corpus

E-VOC (Expressive VOice Control) is a large-scale human-evaluation corpus for studying the instruction-perception gap in instruction-guided text-to-speech (ITTS) systems. It pairs synthesized speech from five ITTS systems with large-scale human ratings across several expressive dimensions, so that the alignment between a user's style instruction and what listeners actually perceive can be measured.

This corpus accompanies the paper:

Do You Hear What I Mean? Quantifying the Instruction-Perception Gap in Instruction-Guided Expressive Text-To-Speech Systems Yi-Cheng Lin, Huang-Cheng Chou, Tzu-Chieh Wei, Kuan-Yu Chen, Hung-yi Lee. Accepted to ICASSP 2026. arXiv:2509.13989.

Overview

ITTS lets users control speech generation through natural-language prompts, but how well listeners perceive the requested style is largely unexplored. E-VOC provides a perceptual analysis of ITTS controllability across two expressive dimensions (adverbs of degree and graded emotion intensity) and adds human ratings on speaker age and word-level emphasis.

ITTS systems

Audio is generated by five systems and stored under the corresponding top-level directories in this repository:

• Parler-TTS-large-v1
• Parler-TTS-mini-v1
• PromptTTS++
• UniAudio
• gpt-4o-mini-tts

Annotation tasks

The dataset is exposed as five splits (one per task) of the default config. Select a split in the Dataset Viewer to browse that task; each row pairs the playable audio clip with its annotation.

Viewer split	Dimension	What annotators judged
task1_adv_degree	Adverbs of degree	Perceived intensity (Very Low … Very High) of an adverb-modulated emotion clip
task2_emotion_intensity	Graded emotion intensity	Perceived intensity of an emotion within an emotion sub-category
task3_emphasis	Word-level emphasis	Which word in the sentence sounds emphasized
task4_age	Speaker age	Perceived speaker age
task5_emotion	Emotion classification	Perceived emotion category of the clip

Tasks 1 and 5 are annotated on the same Adv/emotion audio clips, asking a different question per task (degree vs. emotion category).

Each split is an AudioFolder under data/<split>/ (a metadata.csv plus the split's clips), so the viewer shows an inline audio player and duration distribution next to every annotation. The same content is also available as plain CSVs under labels/ for easy download.

Dataset statistics

File	Annotations	Unique clips	Annotators	Annotation values
Task1_Adv_Degree.csv	17,482	2,880	29	1 - Very Low … 5 - Very High, Unclear
Task2_Emotion_Intensity.csv	29,295	3,600	59	1 - Very Low … 5 - Very High, Unclear
Task3_Emphasis.csv	10,811	1,440	27	emphasized word (50 distinct)
Task4_Age.csv	3,597	720	10	Child, Teenager, Adult, Elderly, Unclear
Task5_Emotion.csv	20,205	2,880	40	Angry, Happy, Sad, Surprised, Neutral, Other, Unclear
Total	81,390	—	144 unique	—

Each clip is rated by multiple annotators. Generation metadata spans 5 ITTS systems x 3 samples x 2 templates x several conversational contexts (e.g. Customer, Family, Friends, Lover, Teacher-Student, Normal).

Columns

Each row (in both the viewer splits and the CSVs) has:

• audio – the playable audio clip.
• Task – task identifier.
• Model, Context, Template, Sample – generation metadata.
• Ground Truth – the intended/target attribute of the clip. Its meaning is task-specific: emotion + degree for Task 1 (e.g. Slightly Sad), emotion intensity for Task 2 (e.g. 2 - Low), the emphasized word for Task 3, the target age for Task 4, and the target emotion for Task 5.
• Annotation – the annotator's response.
• Annotator ID – anonymized annotator pseudonym.
• FileName – the original synthetic clip identifier used during collection.
• Sentence – the carrier sentence read in the clip (present in all tasks).

In the CSV files the audio link is the audio_path column, a repo-relative path pointing into data/<split>/.

Repository layout

• data/<split>/ – the human-annotated clips (one AudioFolder per task) plus the per-split metadata.jsonl. This is the full audio backing every annotation.
• acoustic/<model>/… – the objective acoustic stimuli for Task I (Adverbs of Degree): Adv/pitch, Adv/loudness, and Adv/rate clips (5,400 total). These are analyzed objectively (LUFS / F0 / words-per-second, paper Fig. 1) and have no human ratings.
• labels/ – the same annotations as plain CSVs.

Usage

from datasets import load_dataset

# Load a single task split, with decoded audio
ds = load_dataset("wizzzzzzzzz/E-VOC", split="task1_adv_degree")
row = ds[0]
print(row["Annotation"], row["Sentence"])
print(row["audio"]["sampling_rate"], row["audio"]["array"].shape)

# Or load all five task splits at once
all_tasks = load_dataset("wizzzzzzzzz/E-VOC")
print(all_tasks)  # task1_adv_degree, task2_emotion_intensity, ... task5_emotion

Privacy / anonymization

Original annotator identifiers (Prolific and Amazon Mechanical Turk IDs) are not published. Each Annotator ID is a salted HMAC-SHA256 pseudonym (anon_ + 12 hex chars). The same annotator maps to the same pseudonym across all five files, enabling cross-task analysis, while the secret salt is kept private so the pseudonyms cannot be reversed to real identifiers.

Citation

@inproceedings{lin2026you,
  title={Do You Hear What I Mean? Quantifying the Instruction-Perception GAP in Instruction-Guided Expressive Text-to-Speech Systems},
  author={Lin, Yi-Cheng and Chou, Huang-Cheng and Wei, Tzu-Chieh and Chen, Kuan-Yu and Lee, Hung-yi},
  booktitle={ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={16472--16476},
  year={2026},
  organization={IEEE}
}